Active EMI Suppression System for a 50 mT Unshielded Portable MRI Scanner

Yang, Lei; He, Wei; He, Yucheng; Wu, Jiamin; Shen, Sheng; Xu, Zheng

doi:10.1109/tbme.2022.3170450

Cited by 8 publications

(22 citation statements)

References 23 publications

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“…The 50 mT low-field MRI device was placed on the shielding open area and the active denoising method [4] was utilized to reduce the environmental noise, as shown in Fig. 21.…”

Section: Imaging Results Comparisonmentioning

confidence: 99%

“…The proposed method is based on the conventional EMD method [4], [5], which is summarized as follows: (i) the coil region is subdivided into Q small current loops (considered as magnetic dipoles); (ii) the loop currents, with width a and thickness h are determined such that the generated field matches the target field, and (iii) the coil path is obtained from the optimal current distribution. This procedure is schematically illustrated in Fig.…”

Section: Equivalent Image Magnetic Dipole Methodsmentioning

confidence: 99%

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Gradient Coil Design Method Specifically for Permanent-magnet-type Low Field Portable MRI Brain Scanner

Kong

Shen

et al. 2022

IEEE Trans. Instrum. Meas.

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ferromagnetic structures, particularly the anti-eddy plate, in a bi-planar permanent-magnet-type low-field (0.05 T) magnetic resonance imaging (MRI) brain scanner can distort the gradient field in the target region. This study aims to provide a new gradient coil design method that reduces ferromagnetic influences on gradient field linearity. Thus, a simplified model of electromagnetic (EM) structures of the permanent-magnet-type MRI scanners was established. By using precise analytical proof, the anti-eddy plate was reduced to a homogeneous magnetic plate. The overall effects of the EM structures, which can be represented by bi-planar magnetic plates, were evaluated. In sequence, the image magnetic dipole was first introduced to show the effects of anti-eddy plates were added to the conventional equivalent magnetic dipole (EMD) approach. A novel equivalent image magnetic dipole (EIMD) method was proposed to build the gradient coil pattern. The effect of ferromagnetic materials was predicted throughout the gradient coil design phase using the proposed method, and a high-linear gradient field was generated under real working conditions. The computational and experimental results showed that the gradient coil was linear when ferromagnetic structures were present. The effectiveness of the proposed method was demonstrated by comparing T1-weighted images of the conventional method to those of the proposed method. The proposed method reduced image distortion caused by nearby EM structures in bi-planar permanent-magnet-type lowfield MRI systems and provided an effective and concise solution for gradient coil designs.

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“…The 50 mT low-field MRI device was placed on the shielding open area and the active denoising method [4] was utilized to reduce the environmental noise, as shown in Fig. 21.…”

Section: Imaging Results Comparisonmentioning

confidence: 99%

Section: Equivalent Image Magnetic Dipole Methodsmentioning

confidence: 99%

Gradient Coil Design Method Specifically for Permanent-magnet-type Low Field Portable MRI Brain Scanner

Kong

Shen

et al. 2022

IEEE Trans. Instrum. Meas.

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“…The ECG sensors can also be applied to provide better performance in the in vivo experiments. 17 One limitation of our proposed BLESS method was that only several image contrasts (proton density, T 1 or T 2 *) could be obtained by the spoiled-GRE sequence utilized in this paper. Contrast based on T 2 -weighted imaging can be obtained by the fast spin echo sequences associated with spiral in-out trajectory, in which a variable flip angle scheme should be adopted to maintain a long stable echo train for each spiral arm.…”

Section: Discussionmentioning

confidence: 99%

“…As shown in Figure 1, a homebuilt shielding-free 50-mT MRI system was used to test the validity of our proposed method. 2,17 The main field B 0 was generated by a bipolar permanent magnet with 150-ppm field inhomogeneity. The imaging area was a sphere with a diameter of 200 mm.…”

Section: Mr Scannermentioning

confidence: 99%

“…Our team has designed an active EMI suppression subsystem for the ULF-MRI scanner, and a least square-based method was used to perform EMI cancelation. 17 In this method, transfer coefficients between noise detection and primary MR coils were calculated by the first line of Cartesian k-space data. These outer-part k-space data contain less MR signal and can be considered to be pure noise.…”

Section: Introductionmentioning

confidence: 99%

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Efficient imaging using spiral acquisitions on a portable 50‐mT MR head scanner

et al. 2023

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Ultralow‐field (ULF) magnetic resonance imaging (MRI) can suffer from inferior image quality because of low signal‐to‐noise ratio (SNR). As an efficient way to cover the k‐space, the spiral acquisition technique has shown great potential in improving imaging SNR efficiency at ULF. The current study aimed to address the problems of noise and blurring cancelation in the ULF case with spiral trajectory, and we proposed a spiral‐out sequence for brain imaging using a portable 50‐mT MRI system. The proposed sequence consisted of three modules: noise calibration, field map acquisition, and imaging. In the calibration step, transfer coefficients were obtained between signals from primary and noise‐pick‐up coils to perform electromagnetic interference (EMI) cancelation. Embedded field map acquisition was performed to correct accumulated phase error due to main field inhomogeneity. Considering imaging SNR, a lower bandwidth for data sampling was adopted in the sequence design because the 50‐mT scanner is in a low SNR regime. Image reconstruction proceeded with sampled data by leveraging system imperfections, such as gradient delays and concomitant fields. The proposed method can provide images with higher SNR efficiency compared with its Cartesian counterparts. An improvement in temporal SNR of approximately 23%–44% was measured via phantom and in vivo experiments. Distortion‐free images with a noise suppression rate of nearly 80% were obtained by the proposed technique. A comparison was also made with a state‐of‐the‐art EMI cancelation algorithm used in the ULF‐MRI system. SNR efficiency‐enhanced spiral acquisitions were investigated for ULF‐MR scanners and future studies could focus on various image contrasts based on our proposed approach to widen ULF applications.

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Robust EMI elimination for RF shielding‐free MRI through deep learning direct MR signal prediction

Zhao,

Xiao,

et al. 2024

Magnetic Resonance in Med

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PurposeTo develop a new electromagnetic interference (EMI) elimination strategy for RF shielding‐free MRI via active EMI sensing and deep learning direct MR signal prediction (Deep‐DSP).MethodsDeep‐DSP is proposed to directly predict EMI‐free MR signals. During scanning, MRI receive coil and EMI sensing coils simultaneously sample data within two windows (i.e., for MR data and EMI characterization data acquisition, respectively). Afterward, a residual U‐Net model is trained using synthetic MRI receive coil data and EMI sensing coil data acquired during EMI signal characterization window, to predict EMI‐free MR signals from signals acquired by MRI receive and EMI sensing coils. The trained model is then used to directly predict EMI‐free MR signals from data acquired by MRI receive and sensing coils during the MR signal‐acquisition window. This strategy was evaluated on an ultralow‐field 0.055T brain MRI scanner without any RF shielding and a 1.5T whole‐body scanner with incomplete RF shielding.ResultsDeep‐DSP accurately predicted EMI‐free MR signals in presence of strong EMI. It outperformed recently developed EDITER and convolutional neural network methods, yielding better EMI elimination and enabling use of few EMI sensing coils. Furthermore, it could work well without dedicated EMI characterization data.ConclusionDeep‐DSP presents an effective EMI elimination strategy that outperforms existing methods, advancing toward truly portable and patient‐friendly MRI. It exploits electromagnetic coupling between MRI receive and EMI sensing coils as well as typical MR signal characteristics. Despite its deep learning nature, Deep‐DSP framework is computationally simple and efficient.

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Active EMI Suppression System for a 50 mT Unshielded Portable MRI Scanner

Cited by 8 publications

References 23 publications

Gradient Coil Design Method Specifically for Permanent-magnet-type Low Field Portable MRI Brain Scanner

Gradient Coil Design Method Specifically for Permanent-magnet-type Low Field Portable MRI Brain Scanner

Efficient imaging using spiral acquisitions on a portable 50‐mT MR head scanner

Robust EMI elimination for RF shielding‐free MRI through deep learning direct MR signal prediction

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